– By Mohamed Imran
AI and ML technologies in general, and Generative AI in particular, are expected to have a profound impact on various industries in India over the next decade. According to an EY report, Generative AI could boost India’s GDP by $359-438 billion by 2030, representing a 5.9-7.2% increase, and could add a cumulative US$1.2-1.5 trillion to India’s Gross by FY2029-30. According to MeitY’s IndiaAI, AI is expected to contribute $500 billion to India’s GDP by 2025, accounting for 10% of the country’s target of $5 trillion GDP, and is projected to add $967 billion to the Indian economy by 2035.
According to the same EY report, improved employee productivity, operational efficiency, and more robust customer engagement are key contributors to this impact. However, approximately 75% of organizations express a low to moderate level of readiness to harness the benefits of Generative AI. So how can businesses leverage AI to enhance their performance and stay ahead? Implementing AI is not a simple task. It requires careful planning, execution, and evaluation to ensure that AI delivers the desired outcomes and value. Let’s dive in.
1. Defining Business Objectives
The first step to infuse AI into your business is to define your business objectives and identify where AI can be effective and where it cannot. Generative AI technologies, for instance, can help businesses “generate” text, speech, images, music, video, and code, and can significantly impact businesses by automating content creation, optimizing supply chains, and elevating customer service.
The key question, therefore, is your objective with AI implementation, as it can serve a wide range of functions. You need to have a clear understanding of your business goals, challenges, and opportunities, and map them to the potential use cases and benefits of AI. This will help you prioritize your AI initiatives, align them with your business strategy, and measure their impact.
2. Identify the Right Data Sources
The second step is to identify the right data sources within your organization and ensure they are accurate, relevant, up-to-date, and easy to process. Data is crucial for AI as it serves as the foundation for training AI systems, enabling them to learn, adapt, and make decisions based on the information they’ve been exposed to. Synthetic data can help when data is absent or scarce, and it can be used to supplement training data to improve the accuracy and reliability of AI models.
So, data is a key aspect of your AI strategy, and should include a plan around data collection from right sources, data integration and processing, secure and scalable data storage and systems to monitor and address issues around data quality.
3. Choose the Right AI Stack
Next, you have to choose the right AI stack, which is the combination of tools, frameworks, and models that you will use to build, deploy, and manage your AI solutions. Ideally, this involves a selection of open source AI models, technologies, and advanced AI pipelines that can handle scale. Reliance on closed models and proprietary systems reduce flexibility, create long-term lock-ins, and pose significant business risks. On the other hand, open source models are in your control, and allow you to design your stack in a way that suits your objectives. Variants of LLMs like Mistral, Llama2 or Falcon LLMs, can be used to solve problems around content generation, build customer chatbots, or understand your customer data or documents. On the other hand, you could look at Stable Diffusion models for image generation, or Stable Video Diffusion for video generation, or Meta’s AudioCraft for audio synthesis. There are open source speech recognition systems, and voice synthesis systems that are highly performant as well. You may also need a Vector Database or a Knowledge Graph, to build an AI pipeline that can use your company data and provide context to the AI model.
In essence, your business objectives would define your AI stack, and there are numerous open source options available freely now that help you retain control on your stack.
4. Choose the Right Infrastructure
The fourth step to infuse AI into your business is to choose the right infrastructure where you will run your AI solutions. Choosing the right AI-focussed Hyperscaler can have a significant impact on the delivery and ROI of your AI pipeline, as it can offer you leverages over general-purpose Hyperscalers. For instance, the Hyperscaler should be able to offer you highly performant cloud GPUs and cloud GPU setups like cutting-edge HGX H100 backed by InfiniBand, or advanced cloud GPUs like A100, L4OS etc. Choosing the right AI infrastructure can make or break your AI strategy, as it has a direct impact on cost and can make your AI stack highly efficient. You should also consider the geography where your Hyperscaler is operating from. Since AI often deals with sensitive company data, you should ensure that your data stored in the cloud is safe from intrusion by foreign actors, and fully compliant with laws and regulations of Indian geography.
5. Develop, Train, and Test
The fifth step is to develop, train, and test your AI solutions, which is the process of creating, refining, and validating your AI models. This process involves exploring data characteristics, splitting data into training, and testing data, and eventually fine-tuning your AI models, or building AI pipelines that incorporate your company data into AI’s knowledge base. This is key as it would help you customize the AI stack according to your business objectives, and help it generate results that work in real-world scenarios. Finally, you need to test your models and adapt your strategy based on the results.
6. Deploy in Production/MLOps
The sixth step to infuse AI into your business is to deploy your AI solutions in production, which is the process of making your AI models available and accessible to your end-users or customers. This process involves packaging and deploying the model for inference, serving the model to end-users through integration with your application stack or APIs. To deploy your AI solutions in production effectively, you need to adopt a set of best practices and principles, known as MLOps, which is the application of DevOps to machine learning. MLOps aims to streamline and automate the entire AI lifecycle, from development to deployment to maintenance, and enable collaboration and coordination among different teams and roles, such as data scientists, engineers, and business analysts.
7. Monitor, Refine, and Update
The final step is to monitor, refine, and update your AI solutions, which is the process of keeping your AI solutions relevant, effective, and aligned with your business objectives and user needs. This process involves analyzing user behaviour, and retraining or adapting AI models with updated data. The goal here is to constantly adapt and improve the models so that they improve in accuracy over time.
Final Words
As the AI market continues to grow, businesses that proactively embrace and refine their transformation journey will gain a competitive edge, staying at the forefront of innovation and leveraging the full potential of AI technologies. Businesses should plan this ahead of time, as we are entering the AI decade, when significant changes in business models and workflows are expected. By doing so, they remain competitive and reduce risks in the long run.
(Mohamed Imran is the CTO at E2E Networks.)
(Disclaimer: Views expressed are personal and do not reflect the official position or policy of Financial Express Online. Reproducing this content without permission is prohibited.)
